Can A.I. Predict Earthquakes?
Earthquakes are one of nature’s more unpredictable phenomena. Quakes can cause staggering levels of damage and trigger other natural disasters, like tsunamis. Compounding the effects of the initial quake (called a “mainshock”) are a series of aftershocks – smaller earthquakes that can heighten the existing problems in a quake’s aftermath.
Science has been able to establish laws dictating the magnitude and timing of aftershocks – Omori’s law, Båth's law, and the Gutenberg–Richter law are all accepted by the scientific community as accurate representations of aftershock behavior. But predicting the location of the next quake before it hits has thus far been out of science’s reach. Now, Harvard and Google have leveraged artificial intelligence to predict the location of aftershocks with more accuracy than ever before – and up to a year after the mainshock of an earthquake.
The parties, which consisted of Harvard Department of Earth and Planetary Sciences post-doctoral fellow Phoebe DeVries and Google AI recruiting lead Brendan Meade, as well as additional Google machine learning researchers Martin Wattenberg and Fernanda Viégas, began their analysis by compiling information from 118 “major” earthquakes worldwide. Next, they applied a deep learning technique called a neural net – which teach a computer by analyzing pre-labeled examples from a database to establish patterns corresponding to each label – to that data.
This method enabled researchers to “analyze the relationships between static stress changes caused by the mainshocks and aftershock locations” in a way far more accurate than the pre-existing model (called the Coulomb failure stress change system). Using “a scale accuracy running from 0 to 1 – in which 1 is a perfectly accurate model and 0.5 is as good as flipping a coin”, the new system achieved a 0.849 to the Coulomb system’s 0.583.
The research generated an “unintended consequence” beyond the previously unseen level of accuracy – the ability “to identify physical quantities that may be important in earthquake generation”, creating potential new ways of understanding how earthquakes behave. This piece of the deep learning model is called the von Mises yield criterion – popular “in fields like metallurgy”, it calculates “when materials will begin to break under stress”, and now may have use in earthquake science that was discounted before.
Machine learning may be useful for dredging up previously-ignored insight from existing data, but the system remains imperfect. It is currently too slow to make real-time predictions, and its focus on static (rather than dynamic) stress means it does not present the full scope of potential earthquake prediction. But its improvement over its predecessor is a promising step forward for seismologists and AI researchers alike – with refinement, it could signal a new day in earthquake science.
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Momentum Indicator for GOOGL turns positive, indicating new upward trend
GOOGL saw its Momentum Indicator move above the 0 level on April 17, 2025. This is an indication that the stock could be shifting in to a new upward move. Traders may want to consider buying the stock or buying call options. Tickeron's A.I.dvisor looked at 86 similar instances where the indicator turned positive. In of the 86 cases, the stock moved higher in the following days. The odds of a move higher are at .
Technical Analysis (Indicators)
Bullish Trend Analysis
The RSI Indicator points to a transition from a downward trend to an upward trend -- in cases where GOOGL's RSI Oscillator exited the oversold zone, of 16 resulted in an increase in price. Tickeron's analysis proposes that the odds of a continued upward trend are .
The Moving Average Convergence Divergence (MACD) for GOOGL just turned positive on April 10, 2025. Looking at past instances where GOOGL's MACD turned positive, the stock continued to rise in of 50 cases over the following month. The odds of a continued upward trend are .
Following a 3-day Advance, the price is estimated to grow further. Considering data from situations where GOOGL advanced for three days, in of 358 cases, the price rose further within the following month. The odds of a continued upward trend are .
GOOGL may jump back above the lower band and head toward the middle band. Traders may consider buying the stock or exploring call options.
Bearish Trend Analysis
The Stochastic Oscillator entered the overbought zone. Expect a price pull-back in the foreseeable future.
The 50-day moving average for GOOGL moved below the 200-day moving average on April 08, 2025. This could be a long-term bearish signal for the stock as the stock shifts to an downward trend.
Following a 3-day decline, the stock is projected to fall further. Considering past instances where GOOGL declined for three days, the price rose further in of 62 cases within the following month. The odds of a continued downward trend are .
The Aroon Indicator for GOOGL entered a downward trend on April 11, 2025. This could indicate a strong downward move is ahead for the stock. Traders may want to consider selling the stock or buying put options.
The Tickeron Valuation Rating of (best 1 - 100 worst) indicates that the company is slightly undervalued in the industry. This rating compares market capitalization estimated by our proprietary formula with the current market capitalization. This rating is based on the following metrics, as compared to industry averages: P/B Ratio (6.821) is normal, around the industry mean (11.896). P/E Ratio (26.802) is within average values for comparable stocks, (50.251). Projected Growth (PEG Ratio) (1.626) is also within normal values, averaging (3.572). Dividend Yield (0.000) settles around the average of (0.027) among similar stocks. P/S Ratio (6.435) is also within normal values, averaging (19.917).
The Tickeron Profit vs. Risk Rating rating for this company is (best 1 - 100 worst), indicating low risk on high returns. The average Profit vs. Risk Rating rating for the industry is 88, placing this stock better than average.
The Tickeron SMR rating for this company is (best 1 - 100 worst), indicating very strong sales and a profitable business model. SMR (Sales, Margin, Return on Equity) rating is based on comparative analysis of weighted Sales, Income Margin and Return on Equity values compared against S&P 500 index constituents. The weighted SMR value is a proprietary formula developed by Tickeron and represents an overall profitability measure for a stock.
The Tickeron Price Growth Rating for this company is (best 1 - 100 worst), indicating steady price growth. GOOGL’s price grows at a higher rate over the last 12 months as compared to S&P 500 index constituents.
The Tickeron Seasonality Score of (best 1 - 100 worst) indicates that the company is fair valued in the industry. The Tickeron Seasonality score describes the variance of predictable price changes around the same period every calendar year. These changes can be tied to a specific month, quarter, holiday or vacation period, as well as a meteorological or growing season.
The Tickeron PE Growth Rating for this company is (best 1 - 100 worst), pointing to worse than average earnings growth. The PE Growth rating is based on a comparative analysis of stock PE ratio increase over the last 12 months compared against S&P 500 index constituents.
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a holding company with interests in software, health care, transportation and other technologies
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